1. Field of the Invention
This invention relates to a system and a method for estimating the parameters of a noisy or otherwise disturbed digital transmission channel and for selecting from among a plurality of possible received digital data sequences a sequence most likely to correspond to the actually transmitted sequence.
2. Description of the Related Art
Systems for transmitting digital data are becoming both more common and more sophisticated. As these systems become more widespread, so too does the problem of achieving clear reception even when the transmission channel is disturbed.
Cellular phone systems are but one example of modern digital data transmission systems and they also serve to illustrate the problems associated with such systems. Like other digital transmission systems, a cellular phone system transmits information as a sequence of data symbols or "words." Ideally, the receiver would receive this data sequence one word at a time without interference. Of course, interference is a known fact of life of digital transmission systems.
One example of a typical disturbance phenomenon is known as InterSymbol Interference (ISI), which can occur from fading, reflections, delays, etc., from the cellular phone system, which typically is moving around. Assume that the cellular transmitter has transmitted a first data word immediately followed by a second data word. The receiver may or may not receive the first data word first, but it is very likely in a typical cellular area that the receiver will also receive reflections or otherwise delayed copies (themselves possibly disturbed) of the first data word at the same time that it is receiving the second data word. In other words, one data symbol interferes with another.
The problem of ISI also arises when the receiver only samples the channel once per symbol and has a long impulse response (is narrow-band). In this case, with a long impulse response, the system has a "long memory" and may receive the "tails" of previous symbols as well as what is being transmitted instantaneously.
One should ideally be able to extract the "correct" symbol despite the presence of interference from other symbols. This goal is often frustrated by the fact that the characteristics of the transmission channel itself are either unknown or are at best poorly known. For example, it is all but impossible to know just how cellular telephone signals are being reflected, delayed, faded, and otherwise interfered with as a car is driving around in a typical modern city. Of course, if one does not know the channel, one cannot be sure that what one thinks one has received is actually what was transmitted.
There are accordingly many known methods and systems that attempt to estimate the parameters of a transmission channel in order better to reconstruct a transmitted signal. These channel estimation methods include the well-known Kalman filter and recursive least squares techniques. To estimate a channel using these systems, however, one needs to know the transmitted input signal. One way of accomplishing this is for the transmitter to send a "preamble", which is a known data sequence transmitted at set times. Since the receiver will then know exactly what was transmitted at those set times, it can compare what it received with the known values and thereby estimate the channel. In the system, data itself aids in channel estimation, hence the name "data aided estimation." The drawback of such systems, however, is that they waste bandwidth, since transmission time is taken up by data that has nothing to do with the information one wishes to transmit.
It is well known that the signal one observes can be described as the convolution of the input data with the impulse response function of the channel. Other known systems involve the technique of "equalization," which is equivalent to a deconvolution. The idea in this case is to attempt to generate a transfer function that is the inverse of the channel so that when the signal is transmitted, the inverse function and the channel's own transfer function will "cancel out."
A digital data system of this type can be viewed as a sequence of states. The problem faced by the receiver is to decide which of several possible states is the correct one, that is, the received state that one would have been received if there had been no disturbances on the channel. The estimation systems within the receiver must exclude certain states from consideration according to some procedure. The data states that have not been excluded are typically referred to as "survivors."
One goal of all estimation systems is to arrive at the "best guess" data state in the shortest possible time. The particular definition of "best" used distinguishes many systems from one another.
The well-known Viterbi Algorithm (VA) can be shown to produce the shortest path through the sequence of possible states (the "trellis"). Assuming a discrete convolution, one will then have a finite alphabet that can be transmitted. According to VA, one models the channel memory as a finite state machine. With a finite number of states, there is then a finite search time.
A known variation in VA-type systems avoids the need for a data preamble. Instead, these systems take preliminary data and use it to make a preliminary decision concerning the characteristics of the channel. One problem with this approach is that one typically needs several data samples on which to base a channel estimate, but one can typically not wait that long in order to update the estimate since the channel itself will have changed before the system's estimate is completed. One may encounter this problem any time the delay in updating an estimate is greater than the delay of the channel itself.
Another problem encountered by existing channel estimation systems, including those that use the VA, is that they generate a single, universal estimate of the channel based on the preliminary data, which is often faulty. All survivors are therefore fed by and based on the single estimator, which itself is based on the often faulty preliminary data. The likelihood of each of the "surviving" possible data sequences is then evaluated based on the single channel estimate.
A major drawback of such systems is that they can fall into a "vicious cycle": Often faulty preliminary data is used to construct the single channel estimator, against which all survivors are measured. This reduces the reliability of the survivor selection procedure so that it becomes more likely that a faulty survivor is selected as the "best." This faulty survivor is then fed back and input into the next selection cycle, which drives the estimator even further away from the correct channel model. The estimation may in this way get worse rather than better as the procedure continues.